Methods and systems for characterizing clay

ABSTRACT

A method of improving agricultural treatment includes identifying a mineralogical feature based on a collected soil sample, generating a soil clay characterization based on the mineralogical feature, and generating an agricultural prescription. A system includes a processor and a memory storing instructions that, when executed by the processor, cause the system to identify a mineralogical feature based on a collected soil sample, generate a soil clay characterization based on the mineralogical feature, and generate an agricultural prescription. A non-transitory computer readable medium containing program instructions that, when executed, cause a computer to identify a mineralogical feature based on a soil sample, generate a soil clay characterization based on the mineralogical feature, and generate an agricultural prescription.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/948,613, which was filed on Sep. 24, 2020. U.S. patent applicationSer. No. 16/948,613 is hereby incorporated herein by reference in itsentirety.

TECHNICAL FIELD

The present disclosure is generally directed to methods and systems forcharacterizing clay, and more specifically, for generating fieldmanagement recommendations based on one or more determined clay typeswithin a field and/or sub-field.

BACKGROUND

Growers and trusted advisors struggle to gain a comprehensiveunderstanding of the behavior of clay soils in agricultural fields. Forexample, understanding mineral fraction in terms of percentage of sand,silt and clay is essential for growers seeking to treat their fields. Itis further essential that growers are able to measure and understandsoil organic matter content.

Yet conventional agricultural growing techniques do not take intoaccount the different varieties of clay, including differing latticestructures found in clay based on differing clay content ratios (e.g., a2:1 clay, a 1:1 clay, etc.). For example, a grower/trusted advisor maynot be able to determine soil clay characteristics relating to one ormore fields of the grower. The grower/trusted advisor may sample aportion of one or more fields and discover a clay content type (e.g.,vermiculite) and not realize that other areas of the one or more fieldsinclude additional clay content types. The grower may not be able tomeasure organic matter directly or indirectly.

BRIEF SUMMARY

In one aspect, a computer-implemented method of improving agriculturalinputs/treatment application in clay soils within an agricultural fieldincludes identifying one or more mineralogical features of anagricultural field based on collected soil samples, generating one ormore soil clay characterizations based on the one or more mineralogicalfeatures, each of the soil clay characterizations corresponding to arespective sub-field portion of the agricultural field, and generatingan agricultural prescription for the agricultural field based on the oneor more soil clay characterizations.

In another aspect, a computing system includes one or more processors;and one or more memories storing instructions. When executed by the oneor more processors, the instructions cause the computing system toidentify one or more mineralogical features of an agricultural fieldbased on collected soil samples, generate one or more soil claycharacterizations based on the one or more mineralogical features, eachof the soil clay characterizations corresponding to a respectivesub-field portion of the agricultural field, and generate anagricultural prescription for the agricultural field based on the one ormore soil clay characterizations.

In yet another aspect, a non-transitory computer readable mediumcontains program instructions that, when executed, cause a computer toidentify one or more mineralogical features of an agricultural fieldbased on collected soil samples, generate one or more soil claycharacterizations based on the one or more mineralogical features, eachof the soil clay characterizations corresponding to a respectivesub-field portion of the agricultural field, and generate anagricultural prescription for the agricultural field based on the one ormore soil clay characterizations.

BRIEF DESCRIPTION OF THE FIGURES

The figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

FIG. 1 depicts an exemplary computing environment, according to anembodiment.

FIG. 2 depicts an exemplary crop growing environment depicting claytypes, according to an embodiment.

FIG. 3 depicts an exemplary 2:1 clay, according to one embodiment andscenario.

FIG. 4 depicts an exemplary field map layer corresponding to anagricultural field, according to an embodiment.

FIG. 5 depicts a field map layer depicting multiple determined claytypes corresponding to the agricultural field shown in FIG. 4, accordingto one embodiment and scenario.

FIG. 6 depicts a flow diagram of a computer-implemented method ofimproving agricultural treatment application in clay soils within anagricultural field, according to one embodiment and scenario.

The figures depict preferred embodiments for purposes of illustrationonly. One of ordinary skill in the art will readily recognize from thefollowing discussion that alternative embodiments of the systems andmethods illustrated herein may be employed without departing from theprinciples of the invention described herein.

DETAILED DESCRIPTION Overview

The present disclosure is generally directed to methods and systems forcharacterizing clay, and more specifically, for generating fieldmanagement recommendations based on one or more determined clay typeswithin a field and/or sub-field.

The present techniques enable growers and trusted advisors to get abetter picture of the clay soils in the agricultural fields that theyown and/or manage. A mineral fraction within soil may include sand, siltand/or clay. The organic fractions are referred to as organic matter.Multiple types of clay are common, including 2:1, 1:1, etc. The ratiosreflect the crystalline structure of clay lattices stacked together. Thetypes of clay in a single field may vary greatly from sample to sample.Absorption and shrink-swell capacity are largely affected by clay type.

Clay is a product of geologic activity (e.g., glaciation) and morerecent activity. For example, Lake Erie formerly extended to Fort Wayne,Ind. and into parts of the State of Ohio. Some of the areas in questionwere under water for millennia, and as such did not weather as much assurrounding areas, and thus have generally lower organic matter content.It is the case that clay samples taken from fields in Ohio formerly partof Lake Erie are more similar to clay samples taken from near theMississippi River delta than they are to clay soils found in other partsof Ohio. In other places throughout the Midwest and other breadbasketfarming areas, a 10-foot change in elevation may be evidence of anancient beachhead, and a delimiter of two drastically different claytypes. Consequently, growers can make severe mistakes by assuming thatclay composition of soil is the same throughout a given field or farm.And as will be shown below, clay composition is highly predictive ofagricultural yields, and thus, cannot be ignored by those engaging inthe science of precision agriculture.

The present techniques assist growers and field managers in determiningclay soil types and provide advantageous visualization tools to assistgrowers in discriminating between clay soil types at a field andsub-field (e.g., hexagrid) level, advantageously preventing costlymanagement mistakes. In general, the present techniques includecollecting soil samples for a farm, to determine the clay composition ata granular level (e.g., using 8.5-meter hexagrids). The presenttechniques may compute mineralogical aspects/features, such as shrinkswell potential, and a ratio of organic matter to cation exchangecapacity.

The present techniques include methods and systems for collectingmachine data and for determining clay types within one or moreagricultural fields by analyzing the machine data. In some embodiments,the clay types may be encoded in spatial data files encoded in asuitable file format, such as a commercial or open source shapefile, aGeoJSON format, a Geography Markup Language (GML) file, etc. Suchspatial data files may include one or more layers (i.e., map layers,wherein each layer represents an agricultural characteristic (e.g.,elevation, clay type, etc.). The individual layer(s) and/or files may beshared between multiple computing devices of an agricultural company,provided or sold to customers, stored in a database, etc.

Exemplary Computing Environment

FIG. 1 depicts an exemplary computing environment 100 in which thetechniques disclosed herein may be implemented, according to anembodiment.

The environment 100 includes a client computing device 102, an implement104, a remote computing device 106, and a network 108. Some embodimentsmay include a plurality of client computing devices.

The client computing device 102 may be an individual server, a group(e.g., cluster) of multiple servers, or another suitable type ofcomputing device or system (e.g., a collection of computing resources).For example, the client computing device 102 may be a mobile computingdevice (e.g., a server, a mobile computing device, a smart phone, atablet, a laptop, a wearable device, etc.). In some embodiments theclient computing device 102 may be a personal portable device of a user.In some embodiments the client computing device 102 may be temporarilyor permanently affixed to the implement 104. For example, the clientcomputing device 102 may be the property of a customer, an agriculturalanalytics (or “agrilytics”) company, an implement manufacturer, etc.

The client computing device 102 includes a processor 110, a memory 112and a network interface controller (NIC) 114. The processor 110 mayinclude any suitable number of processors and/or processor types, suchas CPUs, one or more graphics processing units (GPUs), etc. Generally,the processor 110 is configured to execute software instructions storedin a memory 112. The memory 112 may include one or more persistentmemories (e.g., a hard drive/solid state memory) and stores one or moreset of computer executable instructions/modules, including a datacollection module 116, a mobile application module 118, and an implementcontrol module 120, as described in more detail below. More or fewermodules may be included in some embodiments. The NIC 114 may include anysuitable network interface controller(s), such as wired/wirelesscontrollers (e.g., Ethernet controllers), and facilitatebidirectional/multiplexed networking over the network 108 between theclient computing device 102 and other components of the environment 100(e.g., another client computing device 102, the implement 104, theremote computing device 106, etc.).

The one or more modules stored in the memory 112 may include respectivesets of computer-executable instructions implementing specificfunctionality. For example, in an embodiment, the data collection module116 includes a set of computer-executable instructions for collecting amachine data set from an implement (e.g., the implement 104). The datacollection module 116 may include instructions for collecting anabove-ground and/or below-ground soil sample.

The machine data collection module 116 may include a respective set ofinstructions for retrieving/receiving data from a plurality of differentimplements. For example, a first set of instructions may be forretrieving/receiving machine data from a first tractor manufacturer,while a second set of instructions is for retrieving/receiving machinedata from a second tractor manufacturer. In another embodiment, thefirst and second set of instructions may be for, respectively,receiving/retrieving data from a tiller and a harvester. Of course, somelibraries of instructions may be provided by the manufacturers ofvarious implements and/or attachments, and may be loaded into the memory112 and used by the data collection module 116. The data collectionmodule 116 may retrieve/receive machine data from a separate hardwaredevice (e.g., a client computing device 102 that is part of theimplement 104) or directly from one or more of the sensors of theimplement 104 and/or one or more of the attachments 130 coupled to theimplement 104, if any.

The machine data may include any information generated by the clientcomputing device 102, the implement 104 and/or the attachments 130. Insome cases, the machine data may be retrieved/received via the remotecomputing device 106 (e.g., from a third-party cloud storage platform).For example, the machine data may include a clay type generated byanalyzing a soil sample using a soil analysis attachment 130. Themachine data may include sensor measurements of engine load data, fuelburn data, draft, fuel consumption, wheel slippage, etc. The machinedata may include one or more time series, such that one or more measuredvalues are represented in a single data set at a common interval (e.g.,one-second). For example, the machine data may include a first timeseries of draft at a one-second interval, a second time series of wheelslippage, etc.

The machine data may be location-aware. For example, the clientcomputing device 102 may add location metadata to the machine data, suchthat the machine data reflects an absolute and/or relative geographicposition (i.e., location, coordinate, offset, heading, etc.) of theclient computing device 102, the implement 104, and/or the attachments130 within the agricultural field at the precise moment that the clientcomputing device 102 captures the machine data. It will also beappreciated by those of ordinary skill in the art that some sensorsand/or agricultural equipment may generate machine data, that isreceived by the client computing device 102 that already includeslocation metadata added by the sensors and/or agricultural equipment. Inan embodiment wherein the machine data comprises a time series, eachvalue of the time series may include a respective geographic metadataentry. It will be further appreciate by those of ordinary skill in theart that when the machine data is received from a historical archive,the machine data may include historical location data (e.g., the GPScoordinates corresponding to the location from which the historicalmachine data was captured).

The data collection module 116 may receive and/or retrieve the machinedata via an API through a direct hardware interface (e.g., via one ormore wires) and/or via a network interface (e.g., via the network 108).The data collection module 116 may collect (e.g., pull the machine datafrom a data source and/or receive machine data pushed by a data source)at a predetermined time interval. The time interval may be of anysuitable duration (e.g., once per second, once or twice per minute,every 10 minutes, etc.). The time interval may be short, in someembodiments (e.g., once every 10 milliseconds). The data collectionmodule 116 may include instructions for modifying and/or storing themachine data. For example, the data collection module 116 may parse theraw machine data into a data structure. The data collection module 116may write the raw machine data onto a disk (e.g., a hard drive in thememory 112).

In some embodiments, the data collection module 116 may transfer the rawmachine data, or modified machine data, to a remote computingsystem/device, such as the remote computing device 106. The transfermay, in some embodiments, take the form of an SQL insert command. Ineffect, the data collection module 116 performs the function ofreceiving, processing, storing, and/or transmitting the machine data.The data collection module 116 may receive (e.g., from a soil probeattachment) soil sample data corresponding to one or more points withinthe machine data.

The mobile application module 118 may include computer-executableinstructions that display one or more graphical user interfaces (GUIs)on the output device 124 and/or receive user input via the input device122. For example, the mobile application module 118 may correspond to amobile computing application (e.g., an Android, iPhone, or other)computing application of an agrilytics company. The mobile computingapplication may be a specialized application corresponding to the typeof computing device embodied by the client computing device 102. Forexample, in embodiments where the client computing device 102 is amobile phone, the mobile application module 118 may correspond to amobile application downloaded for iPhone. When the client computingdevice 102 is a tablet, the mobile application module 118 may correspondto an application with tablet-specific features. Exemplary GUIs that maybe displayed by the mobile application module 118, and with which theuser may interact, are discussed below.

The mobile application module 118 may include instructions forreceiving/retrieving mobile application data from the remote computingdevice 106. In particular, the mobile application module 118 may includeinstructions for transmitting user-provided login credentials, receivingan indication of successful/unsuccessful authentication, and otherfunctions related to the user's operation of the mobile application. Themobile application module 118 may include instructions forreceiving/retrieving, rendering, and displaying visual maps in a GUI.Specifically, the application module 118 may include computer-executableinstructions for displaying one or more map layers in the output device124 of the client computing device 102. The map layers may depict, forexample, one or more clay types within an agricultural field.

The implement control module 120 includes computer-executableinstructions for controlling the operation of an implement (e.g., theimplement 104) and/or the attachments 130. The implement control module120 may control the implement 104 while the implement 104 and/orattachments 130 are in motion (e.g., while the implement 104 and/orattachments 130 are being used in a farming capacity). For example, theimplement control module 120 may include an instruction that, whenexecuted by the processor 110 of the client computing device 102, causesthe implement 104 to accelerate or decelerate, or collect a soil sampleusing a soil probe.

In some embodiments, the implement control module 120 may cause one ofthe attachments 130 to raise or lower the disc arm of a tiller, or toapply more or less downward or upward pressure on the ground. In someembodiments, the implement control module 120 may control theattachments 130 in response to clay type of the agricultural field wherethe implement 130 is positioned. Practically, the implement controlmodule 120 has all of the control of the implement 104 and/orattachments 130 as does the human operator.

The implement control module 120 may include a respective set ofinstructions for controlling a plurality of implements. For example, afirst set of instructions may be for controlling an implement of a firsttractor manufacturer, while a second set of instructions is forcontrolling an implement of a second tractor manufacturer. In anotherembodiment, the first and second set of instructions may be for,respectively, controlling a tiller and a harvester. Of course, manyconfigurations and uses are envisioned beyond those provided by way ofexample.

In some embodiments, the implement control module 120 may includecomputer-executable instructions for executing one or more agriculturalprescriptions with respect to a field. For example, the control module120 may execute an agricultural prescription that specifies, for a givenagricultural field, a varying application rate of a chemical (e.g., afertilizer, an herbicide, a pesticide, etc.) or a seed to apply atvarious points along the path based on the clay characteristics of thefield. The control module 120 may analyze the current location of theimplement 104 and/or the attachments 130 in real-time (i.e., as thecontrol module 120 executes the agricultural prescription).

In some embodiments, one or more components of the computing device 102may be embodied by one or more virtual instances (e.g., a cloud-basedvirtualization service). In such cases, one or more client computingdevice 102 may be included in a remote data center (e.g., a cloudcomputing environment, a public cloud, a private cloud, etc.). Forexample, a remote data storage module (not depicted) may remotely storedata received/retrieved by the computing device 102. The clientcomputing device 102 may be configured to communicate bidirectionallyvia the network 108 with the implement 104 and/or an attachments 130that may be coupled to the implement 104. The implement 104 and/or theattachments 130 may be configured for bidirectional communication withthe client computing device 102 via the network 108.

The client computing device 102 may receive/retrieve data (e.g., machinedata) from the implement 104, and/or the client computing device 102 maytransmit data (e.g., instructions) to the implement 104. The clientcomputing device 102 may receive/retrieve data (e.g., machine data) fromthe attachments 130, and/or may transmit data (e.g., instructions) tothe attachments 130. The implement 104 and the attachments 130 will nowbe described in further detail.

The implement 104 may be any suitable powered or unpoweredequipment/machine or machinery, including without limitation: a tractor,a combine, a cultivator, a cultipacker, a plow, a harrow, a stripper, atiller, a planter, a baler, a sprayer, an irrigator, a sorter, anharvester, etc. The implement 104 may include one or more sensors (notdepicted) including one or more soil probe and the implement 104 may becoupled to one or more attachments 130. For example, the implement 104may include one or more sensors for measuring respective implementvalues of engine load data, fuel burn data, draft sensing, fuelconsumption, wheel slippage, etc. Many embodiments including more orfewer sensors measuring more or fewer implement values are envisioned.The implement 104 may be a gas/diesel, electric, or hybrid vehicleoperated by a human operator and/or autonomously (e.g., as anautonomous/driverless agricultural vehicle).

The attachments 130 may be any suitable powered or unpoweredequipment/machinery permanently or temporarily affixed/attached to theimplement 104 by, for example, a hitch, yoke or other suitablemechanism. The attachments 130 may include any of the types of equipmentthat the implement 104 may comprise (e.g., a tiller). The attachments130 may include one or more sensors (not depicted) that may differ innumber and/or type according to the respective type of the attachments130 and the particular embodiment/scenario. For example, a tillerattachment 120 may include one or more soil coring probes. It should beappreciated that many attachments 130 sensor configurations areenvisioned. For example, the attachments 130 may include one or morecameras. The attachments 130 may be connected to the implement 104 viawires or wirelessly, for both control and communications. For example,attachments 130 may be coupled to the client computing device 102 of theimplement 104 via a wired and/or wireless interface for datatransmission (e.g., IEEE 802.11, WiFi, etc.) and main/auxiliary control(e.g., 7-pin, 4-pin, etc.). The client computing device 102 maycommunicate bidirectionally (i.e., transmit data to, and/or receive datafrom) with the remote computing device 106 via the network 108.

The client computing device 102 includes an input device 122 and anoutput device 124. The input device 122 may include any suitable deviceor devices for receiving input, such as one or more microphone, one ormore camera, a hardware keyboard, a hardware mouse, a capacitive touchscreen, etc. The output device 124 may include any suitable device forconveying output, such as a hardware speaker, a computer monitor, atouch screen, etc. In some cases, the input device 122 and the outputdevice 124 may be integrated into a single device, such as a touchscreen device that accepts user input and displays output. The clientcomputing device 102 may be associated with (e.g., leased, owned, and/oroperated by) an agrilytics company.

The network 108 may be a single communication network, or may includemultiple communication networks of one or more types (e.g., one or morewired and/or wireless local area networks (LANs), and/or one or morewired and/or wireless wide area networks (WANs) such as the Internet).The network 108 may enable bidirectional communication between theclient computing device 102 and the remote computing device 106, orbetween multiple client computing devices 102, for example.

The remote computing device 106 includes a processor 140, a memory 142,and a NIC 144. The processor 140 may include any suitable number ofprocessors and/or processor types, such as CPUs and one or more graphicsprocessing units (GPUs). Generally, the processor 140 is configured toexecute software instructions stored in the memory 142. The memory 142may include one or more persistent memories (e.g., a hard drive/solidstate memory) and stores one or more set of computer executableinstructions/modules, as discussed below. For example, the remotecomputing device 106 may include a data processing module 150, atopographic module 152, a mineral composition module 154, a claycharacterization module 156, and a prescription module 158. The NIC 144may include any suitable network interface controller(s), such aswired/wireless controllers (e.g., Ethernet controllers), and facilitatebidirectional/multiplexed networking over the network 106 between theremote computing device 106 and other components of the environment 100(e.g., another remote computing device 106, the client computing device102, etc.).

The one or more modules stored in the memory 142 may include respectivesets of computer-executable instructions implementing specificfunctionality. For example, in an embodiment, the data processing module150 includes computer-executable instructions for receiving/retrievingdata from the client computing device 102, the implement 104, and/or theattachments 130. For example, the data processing module 150 may includeinstructions that when executed by the processor 140, cause the remotecomputing device 106 to receive/retrieve machine data. The dataprocessing module 150 may include further instructions for storing themachine data in one or more tables of the database 180. The dataprocessing module 150 may store raw machine data, or processed data.

The data processing module 150 may include instructions for processingthe raw machine data to generate processed data. For example, theprocessed data may be data that is represented using data types data ofa programming language (e.g., R, C#, Python, JavaScript, etc.). The dataprocessing module 150 may include instructions for validating the datatypes present in the processed data. For example, the data processingmodule 150 may verify that a value is present (i.e., not null) and iswithin a particular range or of a given size/structure. In someembodiments, the data processing module 150 may transmit processed datafrom the database 180 in response to a query, or request, from theclient computing device 102. The data processing module 150 may transmitthe processed data via HTTP or via another data transfer suitableprotocol.

The topographic module 152 may include instructions for retrievingand/or providing mapping data (e.g., electronic map layer objects) toother modules in the remote computing device 106. The mapping data maytake the form of raw data (e.g., a data set representing claycomposition map for a spatial area). In some embodiments, thetopographic module may include spatial data files. The topographicmodule 152 may store mapping data in, and retrieve mapping data from,the database 180. The topographic module 152 may source elevation datafrom public sources, such as the United States Geological Survey (USGS)National Elevation Dataset (NED) database. In some embodiments, the dataprocessing module 150 may provide raw data to the topographic module152, wherein instructions within the topographic module 152 infer theelevation of a particular tract of land by analyzing the raw data. Theelevation data may be stored in a two-dimensional (2D) orthree-dimensional (3D) data format, depending on the embodiment andscenario.

The mineral composition module 154 may include instructions foranalyzing one or more machine data variables to identify mineralogicalfeatures. As discussed, the machine data generated by the implement 104and/or the attachments 130 may include measurements corresponding to asoil probe. The mineralogical features may be identified directly, byanalyzing soil samples, or by analyzing other data (e.g., historicalmachine data). For example, the soil probe may be used to generate acation exchange capability value, an organic matter measurement, etc.with respect to a plurality of locations, or points, within a field. Insome embodiments the points may correspond to a respective hexagonalgrid cell within a tiled cell (i.e., a hexagrid). The mineralcomposition module may associate a soil sample with a respectivehexagrid. In some embodiments, associating the soil sample may includereading machine data corresponding to the soil sample from theelectronic soil probe. The mineral composition module may associatelocation data with the machine data corresponding to the soil sample.

The clay characterization module 156 may include analyzing themineralogical features and/or machine data to determine claycharacteristics. For example, the clay characterization may characterizea soil sample as belonging to a clay group, such as kaolin, smectite,illite, chlorite, etc. In some embodiments, the clay characterizationmodule 156 may further identify the specific clay within the clay group,such as montmorillonite, nacrite, etc. In some embodiments, the claycharacterization module 156 may determine clay characteristics of a soilsample by referencing a digital map layer, such as one provided by thetopographic module 152. In some embodiments, the mineral compositionmodule 154 and/or the clay characterization module 156 may analyzemachine data using one or more trained machine learning models todetermine clay characteristics of the field. In yet other embodiments,the clay characterization module 156 may provide a quantitative index ofsoil clay activity. For example, a soil clay characterization mayinclude identification and/or quantification of soil type within a givenhexagrid of a land tract. A single hexagrid may include multiple soilclay characterizations, each including respective quantifications of thesoil clay activity.

The prescription module 158 includes computer-executable instructionsfor generating one or more agricultural prescriptions. The agriculturalprescriptions may be a set of computer-executable instructions forperforming one or more agricultural interventions with respect to anagricultural field. For example, the agricultural prescription mayinclude one more map layers specifying a respective set of interventionsrelating to seeding, fertilization, tillage, etc. The client computingdevice 102 may receive/retrieve the prescription instructions, andexecute them.

The prescription module 158 may include generating one or moreagricultural prescriptions, or scripts. The agricultural prescriptionsmay include computer-executable instructions for causing an implement(e.g., the implement 130) to perform one or more tasks (e.g., dispense amacronutrient fertilizer at a predetermined and/or variable rate). Insome embodiments, the prescription may include instructions forperforming the tasks in response to a clay type at a location within agiven field. For example, the implement control module 120 may analyze afield map layer received from the topographic module 152 and a clay maplayer received from the clay characterization module 156. The implementcontrol module 120 may execute the prescription. The prescription mayinclude instructions causing the implement 130 to perform the task in apredetermined way (e.g., apply fertilizer at 0.1 gallons/second) when 1)the location of the implement 130 coincides with a clay type in the claymap layer, as determined by reference to the clay map layer; and 2) thelocation of the implement 130 coincides with a particular field, asdetermined by reference to the field map layer. In this way, theprescription module 158 may generate prescriptions executable by aclient device for modifying a clay soil to include, for example, more ofa given macronutrient (e.g., potassium).

The prescription module 158 may be generated by a suitable tool. Forexample, in some embodiments, the remote computing device 106 mayinclude a further module that allows the user to specify the number ofyears desired to build soil potassium to a critical/target level. Theprescription module 158 may include instructions for calculating, basedon the target rate and the current rate as seen in the machine data, anamount of macronutrient to apply to cause the soil to reach the targetvalue.

The remote computing device 106 may further include one or moredatabases 180, an input device 182, and an output device 184. Thedatabase 180 may be implemented as a relational database managementsystem (RDBMS) in some embodiments. For example, the data store 180 mayinclude one or more structured query language (SQL) databases, a NoSQLdatabase, a flat file storage system, or any other suitable data storagesystem/configuration. In general, the database 180 allows the clientcomputing device 102 and/or the remote computing device 106 to create,retrieve, update, and/or retrieve records relating to performance of thetechniques herein. For example, the database 180 may allow the clientcomputing device 102 to store information received from one or moresensors of the implement 104 and/or the attachments 130. The database180 may include a Lightweight Directory Access Protocol (LDAP)directory, in some embodiments. The client computing device 102 mayinclude a module (not depicted) including a set of instructions forquerying an RDBMS, an LDAP server, etc. For example, the clientcomputing device 102 may include a set of database drivers for accessingthe database 180 of the remote computing device 106. In someembodiments, the database 180 may be located remotely from the remotecomputing device 104, in which case the remote computing device 104 mayaccess the database 180 via the NIC 112 and the network 106.

The input device 182 may include any suitable device or devices forreceiving input, such as one or more microphones, one or more cameras, ahardware keyboard, a hardware mouse, a capacitive touch screen, etc. Theinput device 182 may allow a user (e.g., a system administrator) toenter commands and/or input into the remote computing device 106, and toview the result of any such commands/input in the output device 184. Forexample, an employee of the agrilytics company may use the input device182 to adjust parameters with respect to one or more agricultural fieldsfor applying macronutrients via a prescription.

The output device 184 may include any suitable device for conveyingoutput, such as a hardware speaker, a computer monitor, a touch screen,etc. The remote computing device 106 may be associated with (e.g.,leased, owned, and/or operated by) an agrilytics company. As notedabove, the remote computing device 106 may be implemented using one ormore virtualization and/or cloud computing services. One or moreapplication programming interfaces (APIs) may be accessible by theremote computing device 106.

In operation, the agrilytics company may access the remote computingdevice 106 to establish one or more field records on behalf of one ormore growers. For example, the company may store the field records inthe database, wherein each grower is associated with a unique identifier(e.g., a universally unique identifier (UUID)) as are each of thegrower's respective fields. For example, the grower may be associatedwith the grower's fields in the database via a one-to-many relationship.

The agrilytics company may populate the database 180 with machine datacorresponding to the grower's fields by using the implement 104 to drivethe fields and collect the machine data. The machine data may includeinformation gathered from an attachment 130 (e.g., a soil probe) and/ormachine data collected from other sources. Once the machine data for thegrower's fields has been collected, the mineral composition module 154may analyze the machine data to determine mineralogical features of thegrower's one or more fields. The clay characterization module 156 mayanalyze the mineralogical features and determine one or more clay typescorresponding to the field. The clay types may be assigned to one ormore hexagrids within the field.

The prescription module 158 may include instructions that analyze themineralogical composition and clay content of the field and determineone or more treatments for affecting portions of the field. For example,the prescription module 158 may be pre-programmed to increase thepotassium content of each hexagrid to a specified critical level. Whenthe organic matter percentage of a given hexagrid is below thethreshold, the prescription module 158 may include instructions foradding organic fertilizer to the hexagrid. The instructions for addingorganic fertilizer may vary based on the clay type of the hexagrid. Forexample, in a clay type having higher cation exchange potential, theprescription module 158 may cause less fertilizer to be applied toincrease the potassium level, as compared to another hexagrid having alower cation exchange potential. It should be appreciated that theexamples provided are intentionally simplified for explanation, and manyfurther embodiments are envisioned, as described below.

Exemplary Clay Sampling Embodiments

FIG. 2 depicts an exemplary crop growing environment 200 depicting claytypes, according to an embodiment. The crop growing environment 200includes a crop 202, a plurality of clay lattices 204 and a plurality ofcation exchange pathways 206. The crop 202 may be, for example, a cerealcrop (e.g., corn, wheat, rice, etc.) a tuber crop (e.g., sweet potato),a vegetable crop (e.g., onion, tomato, etc.), a fruit crop (e.g.,mangoes, grapes, etc.). The crop 202 may include an above-ground portionand/or a below-ground portion.

The plurality of clay lattices 204 may include a clay lattice 204-A thatincludes free/exchangeable cations (e.g., potassium cations (K₊)). Theexchangeable cations may be located between/within layers, or sheets, ofthe clay lattice 204-A and/or about the sheets composing the claylattice 204-A. The clay lattice 204-A may be a smectitic clay such asmontmorillonite, kaolinite, etc. The clay lattice 204-A may exchange theexchangeable cations with the crop 202 via the cation exchange pathway206-A, and/or with other clay lattices via the cation exchange pathway206-C.

The plurality of clay lattices 204 may include a clay lattice 204-B thatincludes fixed, or captured, cations. For example, the captured cationsmay include potassium (K+) cations. The clay lattice 204-B may be asilicate clay (e.g., a mica clay, a vermiculite clay, a 2:1 clay, etc.)that traps the exchangeable cations, preventing the cations from beingexchanged along the cation exchange pathway 206-B or the cation exchangepathway 206-C. The cations may be trapped within the clay lattices asdiscussed below. In some cases, the clay lattice 204-B may allow thecations to be released, albeit more slowly. The lack of exchange may bereferred to as cation fixation.

The plurality of exchange pathways 206 within the crop growingenvironment 200 allow the cations to be exchanged between the claylattices 204-A and the crop 202. For example, the crop 202 may uptakepotassium ions from the clay lattice 204-A. The rate of uptake maydetermine the level of a given macronutrient available to the crop 202.In other cases, soluble cations may be leached away from the crop 202and/or the plurality of clay lattices 204. In still further casessoluble cations may be unavailable due to depleted plant availablewater. The present techniques may include measuring and treating theenvironment 200, for example, to add macronutrients in the form offertilizer (e.g., a potassium fertilizer).

FIG. 3 depicts an exemplary 2:1 clay 302, according to one embodimentand scenario. The 2:1 clay 302 includes a plurality of tetrahedralsheets 304 and a plurality of octahedral sheets 306, in a ratio of twotetrahedral sheets per one octahedral sheet. When the 2:1 clay 302 is apotassium fixating clay, as discussed with respect to FIG. 2, the 2:1clay 302 may include a plurality of cations 308 (e.g., potassium ions).The plurality of cations may be captured, or fixed, between two or moreof the plurality of tetrahedral sheets 304. The plurality of tetrahedralsheets 304 and the plurality of octahedral sheets 306 in a ratio of 2:1may cause cation macronutrients needed for growth of a crop (e.g., thecrop 202) to be trapped in holes, or openings, in the lattice structureof the 2:1 clay 302. In some clays, nutrients can move in and out.However, when the clays become dehydrated, holes within the clayscollapse, trapping some ions (e.g., potassium) but allowing others to befreed (e.g., calcium, magnesium, etc.). The ability of the clay to holdparticular ions depends on the size of gaps in the crystalline structureof the clays.

As noted, the present techniques may include analyzing properties of theplurality of cations 308 using a soil probe implement. For example, inan embodiment, the implement 104 may include a probe attachment 130 thatsamples the soil of the field at different points. The sampling mayinclude generating a dataset corresponding to the field, divided intohexagonal regions (e.g., a set of one or more hexagrids). The samplingmay include analyzing one or more samples within each hexagrid todetermine the absorptive properties (e.g., exchange free energy) of eachsample. By computing such properties, the present techniques may be usedto determine an appropriate treatment regime and/or to compare eachsample to other measured soils. For example, a montmorillonite soil inMissouri may be compared to a montmorillonite soil in Indiana. In someembodiments, the present techniques may include analyzing additionalfactors of the field (e.g., historical field intervention, weather data,etc.).

Exemplary Clay Mapping Embodiments

FIG. 4 depicts an exemplary field map layer 400 corresponding to anagricultural field. The field map layer 400 may correspond to a digitalmap layer downloaded from, for example, the remote computing device 106of FIG. 1. The field map layer 400 may be a digital map layer displayedin a device (e.g., the client computing device 102 of FIG. 1). The fieldmap layer 400 may correspond to a field being driven by the implement104, for example. The implement 104 may drive the field corresponding tothe field map layer 400, collecting one or more soil samples from theagricultural field. The field map layer 400 may include a field region402, a field region 404, a field region 406 and a field region 408. Thefield map layer 400 may include any suitable number of regions in someembodiments. The regions may be encoded using hexagrids. For example,the hexagrid 402 may be subdivided into a plurality of one or morehexagrids (e.g., 8.5-m hexagonal cells).

The field map layer 400 may include one or more subsurface field regions410 and one or more bedrock field regions 412. The subsurface fieldregions 410 and bedrock field regions 412 may be depicted as separatemap layers in some embodiments. The field map layer 400 may include awaterway field region 414. The waterway field region 414 may correspondto a portion of a flowing surface waterway, such as a stream, river,delta, swamp, etc. In some embodiments, the waterway field region 414may correspond to a historical waterway, such as an alluvium or estuarycreated during a glacial period.

The field map layer 400 may be generated by sampling the agriculturalfield. For example, the grower may drive the field corresponding to thefield map layer 400 and collect a plurality of samples of theagricultural field, including respective machine data. For example, thefield may be sampled according to the hexagrid subdivisions, such thatone or more soil probe samples are collected from each hexagonalsubdivision of the agricultural field. The present techniques mayinclude analyzing the samples to determine one or more clay types amongthe regions of the field, as shown in FIG. 5.

FIG. 5 depicts a field map layer 500 depicting multiple determined claytypes corresponding to the agricultural field shown in FIG. 4. Forexample, a field region 502 may correspond to the waterway field region414 of FIG. 4. The present techniques may include identifying one ormore clay soil types present within the field region 502 that may dependon the geologic features of the field region 502. For example, when thewaterway field region 414 corresponds to an historical alluvial area,heavy 2:1 clay soil may be found. The heavy 2:1 clay soil may include avery fine soil with minute particle sizes that are finely precipitated.Another region, such as the field region 504 may be found to have soilwith a greater amount of silt, resulting in a silty clay loam. Yetanother field region such as the field region 506 may be a weatheredregion that is closer to a 1:1 clay.

A given field (e.g., the field corresponding to the field map layer 500)may include a mixture of clays of significantly varying ratios, evenwithin a relatively small growing area (e.g., a 10-acre farm). Suchvariability may be the result of distant (e.g., glacial) activity and/ormore recent activity (e.g., weathering) caused by areas at lower-lyingelevation receiving more standing water.

By identifying the differences in clay types, the present techniques mayprevent growers from making a large mistake due to varyingcation-exchange capability and/or water holding properties of thevarious clays, including the absorption and shrink-swell capacity of theclays in respective field regions. Regardless of the cause of suchvariation, it is essential that growers are able to quickly appreciatethe differences in soil properties, such as organic matter to cationexchange capability ratio. For example, soil in Illinois regularlymeasures 3.5% organic matter with cation exchange capability of +20. Inthe Mississippi delta, organic matter may measure 2% (or less) withcation exchange capability of >=30. The present techniques improvegrower yields and agricultural product performance by allowing thegrower to generate large data sets for representing clay typeinformation through automated collection and processing of machine data,and for generating visualizations based on that collection/processing toallow the grower to understand how clay types will cause cations to beexchanged/bound up in soil, and to automatically generate prescriptionsto modify the soil composition.

The field map layer 500 including identified clay types may be displayedin a graphical user interface (GUI). For example, the GUI may bedisplayed in the mobile application 118. The GUI may also depict one ormore pieces of growing equipment, such as the implement 104. Thus, whenthe grower is driving the field, the GUI may depict the location of thegrower and/or the implement with respect to the one or more fieldregions (e.g., the field region 504). The present techniques may includeusing the GUI for autonomous and/or operator-controlled application ofone or more field interventions, such as during the execution ofagricultural prescriptions. The present techniques advantageously allowthe grower to visualize differing clay types within areas of theagricultural field.

Exemplary Computer-Implemented Methods

FIG. 6 depicts a flow diagram of an example computer-implemented method600 for improving agricultural treatment application in clay soilswithin an agricultural field, according to one embodiment and scenario.

The method 600 may include collecting a machine data set correspondingto the agricultural field (block 602). The machine data setcorresponding to the agricultural field may include one or moremeasurements taken using a soil probe. The soil probe may includemanual, hydraulic and/or electronic aspects, in some embodiments andscenarios. Specifically, the implement 104 may collect machine datausing the soil probe. In some embodiments, the collected machine dataset may include historical machine data collected previously. Thehistorical machine data may be collected by the implement 104 or anotherprocess/actor, in some embodiments. The machine data may include datacollected from multiple mechanisms (e.g., from farm equipment, from oneor more soil probes, and/or other sources).

The method 600 may include analyzing the machine data set to identifyone or more mineralogical features (block 604). The mineralogicalfeatures may include shrink swell potential, organic matter content,cation exchange capacity, soil texture, and/or other physical propertiesand/or chemical properties of the soil, nitrogen mineralization, porespace size, water content, alkalinity, salinity, etc. In someembodiments, analyzing the machine data may include processing themachine data using a set of instructions for classifying themineralogical features into one or more categories using a trainedmachine learning model (e.g., a classification machine learning model).The method 600 may include training the machine learning model using alabeled data set (e.g., in a machine learning module of the remotecomputing device 106). The method 600 may include operating the machinelearning model using the machine data as input. Those of ordinary skillin the art will appreciate that other means for analyzing the machinedata set, including computer vision-based approaches and/or otheroptical techniques may be used in some embodiments for generating somemineralogical features.

The method 600 may include generating a set of clay characterizations byanalyzing the mineralogical features, each of the characterizationscorresponding to a respective hexagrid cell (block 606). As noted above,the collection of machine data may be performed by an implement (e.g.,the implement 104) and/or by retrieving/receiving digital machine data.In either case, the method 600 may annotate each data point within themachine data with a geographic position. The geographic position of eachpoint may be added to the machine data upon collection by an implementand/or a computing device (e.g., by an onboard Global Positioning System(GPS) device of the implement 130 or the client computing device 106).Once annotated with location information, the machine data can be fixedwithin a field grid. In some embodiments, it may be advantageous tosubdivide the field (e.g., the field corresponding to the field maplayer 600 of FIG. 6) into hexagonal grids (e.g., 8.5-meter hexagrids).

The method 600 may associate each hexagrid with the generated claycharacterizations, such that once the method 600 has been completed,each of the hexagrids within the field include a respective set of claycharacterizations. In this way, the grower, field manager, trustedadvisor or other relevant party can view the field map layer showingrespective clay characteristics for the field, as shown in FIG. 6. Theability to view differing clay characteristics is advantageous forpractical growing purposes. In the most basic example, the groweroperating the implement 104 may view the field map layer including claycharacterizations and manually dispense more fertilizer in areas thatare of a clay type likely to include more cation fixation. In otherwords, dispense more product in an area of the field likely to havelesser availability of a particular macronutrient. In furtherembodiments, the field map layer may be annotated with indications incolor or using another suitable style depicting clay with likely goodlevels of macronutrient (e.g., a green color), reduced but stillacceptable levels of macronutrient (e.g., a yellow color) and lackinglevels of macronutrient (e.g., a red color). Therefore, even if thegrower does not appreciate the relationship between clay characteristicsand macronutrient profile, the visual indicators provide informationsufficient for good decision making and improved economy of fertilizersand other treatments, in addition to improved yield per acre.

As discussed above, the method 600 may include generating andtransmitting (e.g., from the remote computing device 106) the map layersvia the network 108 for display in the client computing device 106. Instill further embodiments, the present techniques may be used,optionally in conjunction with other non-clay characteristics maplayers, to automate the application of agricultural treatments.

For example, the method 600 may include generating an agriculturalprescription for the agricultural field, including at least onetreatment based on the clay characterization (block 608). As discussed,the skilled grower will appreciate that certain clay types may include aclay lattice (e.g., the clay lattice 204-B) that includes fixed, orcaptured, cations. These clays may be 2:1 clays or other clay types thatmay inhibit the movement of nutrients necessary for plant growth. And asnoted, the grower could manually activate a treatment by, for example,causing a sprayer to apply fertilizer based on the grower's monitoringof a GUI while driving the field.

However, the present techniques represent a further advantageousimprovement over conventional techniques that require the grower tomaintain constant attention during the laborious planting and harvestseasons, that may be further challenging due to hot/cold weather,precipitation and, in many cases, working in darkness. To that end, themethod 600 may include performing the generated agriculturalprescription by, for example, transmitting the prescription in the formof an electronic prescription file to the client computing device 102for execution in the implement control module 120. The agriculturalprescription may include sets of instructions for automatically applyinga treatment in portions of the field that correspond with certain claycharacteristics.

The agricultural prescription may access a location module (e.g., a GPSmodule) of the client computing device 102 to determine the real-timeposition of the implement within the field, with respect to the fieldmap layer associated with clay characteristics information. Theagricultural prescription may include instructions for causing apre-determined agricultural treatment to be applied to the field, forexample by accessing an attachment (e.g., the attachment 130 of FIG. 1).In this way, the present techniques may be advantageously used toidentify the clay soil characteristics of a field, which may be highlyvariable for the reasons discussed above. The present techniques mayfurther advantageously be used to automatically apply treatment productbased on the clay soil characteristics, to conserve product whileincreasing yields.

ADDITIONAL CONSIDERATIONS

The following considerations also apply to the foregoing discussion.Throughout this specification, plural instances may implement operationsor structures described as a single instance. Although individualoperations of one or more methods are illustrated and described asseparate operations, one or more of the individual operations may beperformed concurrently, and nothing requires that the operations beperformed in the order illustrated. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term” “is herebydefined to mean . . . ” or a similar sentence, there is no intent tolimit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based on any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this patent isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only so as to not confuse the reader,and it is not intended that such claim term be limited, by implicationor otherwise, to that single meaning. Finally, unless a claim element isdefined by reciting the word “means” and a function without the recitalof any structure, it is not intended that the scope of any claim elementbe interpreted based on the application of 35 U.S.C. § 112(f).

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of “a” or “an” is employed to describe elements andcomponents of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of ordinary skill in the art willappreciate still additional alternative structural and functionaldesigns for implementing the concepts disclosed herein, through theprinciples disclosed herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those of ordinaryskill in the art, may be made in the arrangement, operation and detailsof the method and apparatus disclosed herein without departing from thespirit and scope defined in the appended claims.

What is claimed:
 1. A computer-implemented method of improvingagricultural treatment application in clay soils within an agriculturalfield, the method comprising: identifying one or more mineralogicalfeatures of an agricultural field based on collected soil samples;generating one or more soil clay characterizations based on the one ormore mineralogical features, each of the soil clay characterizationscorresponding to a respective sub-field portion of the agriculturalfield; and generating an agricultural prescription for the agriculturalfield based on the one or more soil clay characterizations.
 2. Thecomputer-implemented method of claim 1, wherein the soil samplescorrespond to one or more measurements taken using a soil probe.
 3. Thecomputer-implemented method of claim 1, wherein the one or moremineralogical features include a ratio of soil organic matter to cationexchange capacity.
 4. The computer-implemented method of claim 1,further comprising: displaying, in a client computing device, a fieldmap layer in a graphical user interface, the field map layer depicting atopology of the agricultural field.
 5. The computer-implemented methodof claim 1, further comprising: displaying, in a client computingdevice, a clay map layer in a graphical user interface, the clay maplayer depicting the one or more soil clay characteristics within theagricultural field.
 6. The computer-implemented method of claim 1,wherein generating the agricultural prescription for the agriculturalfield includes identifying a nutrient to be added to affect thepotential availability of the nutrient in the agricultural field.
 7. Thecomputer-implemented method of claim 1, further comprising: determininga geographic position of a growing implement and, when the growingimplement is within the agricultural field, causing a pre-determinedagricultural treatment to be applied to the agricultural field based onthe agricultural prescription.
 8. A computing system comprising: one ormore processors; and one or more memories storing instructions that,when executed by the one or more processors, cause the computing systemto: identify one or more mineralogical features of an agricultural fieldbased on collected soil samples; generate one or more soil claycharacterizations based on the one or more mineralogical features, eachof the soil clay characterizations corresponding to a respectivesub-field portion of the agricultural field; and generate anagricultural prescription for the agricultural field based on the one ormore soil clay characterizations.
 9. The computing system of claim 8,the one or more memories storing further instructions that, whenexecuted, cause the computing system to: collect the soil samples bytaking one or more measurements using a soil probe.
 10. The computingsystem of claim 8, wherein the one or more mineralogical featuresinclude a ratio of soil organic matter to cation exchange capacity. 11.The computing system of claim 8, the one or more memories storingfurther instructions that, when executed, cause the computing system to:display, in a client computing device, a field map layer in a graphicaluser interface, the field map layer depicting a topology of theagricultural field.
 12. The computing system of claim 8, the one or morememories storing further instructions that, when executed, cause thecomputing system to: display, in a client computing device, a clay maplayer in a graphical user interface, the clay map layer depicting theone or more soil clay characteristics within the agricultural field. 13.The computing system of claim 8, the one or more memories storingfurther instructions that, when executed, cause the computing system to:generate the agricultural prescription for the agricultural field byidentifying a nutrient to be added to affect the potential availabilityof the nutrient in the agricultural field.
 14. The computing system ofclaim 8, the one or more memories storing further instructions that,when executed, cause the computing system to: determine a geographicposition of a growing implement; and, cause, when the growing implementis within the agricultural field, a pre-determined agriculturaltreatment to be applied to the agricultural field by executing theagricultural prescription.
 15. A non-transitory computer readable mediumcontaining program instructions that, when executed, cause a computerto: identify one or more mineralogical features of an agricultural fieldbased on collected soil samples; generate one or more soil claycharacterizations based on the one or more mineralogical features, eachof the soil clay characterizations corresponding to a respectivesub-field portion of the agricultural field; and generate anagricultural prescription for the agricultural field based on the one ormore soil clay characterizations.
 16. The non-transitory computerreadable medium of claim 15, containing further program instructionsthat, when executed, cause the computer to: collect the soil samples bytaking one or more measurements using a soil probe.
 17. Thenon-transitory computer readable medium of claim 15, wherein the one ormore mineralogical features include a ratio of soil organic matter tocation exchange capacity.
 18. The non-transitory computer readablemedium of claim 15, containing further program instructions that, whenexecuted, cause the computer to: display, in a client computing device,a clay map layer in a graphical user interface, the clay map layerdepicting the one or more soil clay characteristics within theagricultural field.
 19. The non-transitory computer readable medium ofclaim 15, containing further program instructions that, when executed,cause the computer to generate the agricultural prescription for theagricultural field by identifying a nutrient to be added to affect thepotential availability of the nutrient in the agricultural field. 20.The non-transitory computer readable medium of claim 15, containingfurther program instructions that, when executed, cause the computer to:determine a geographic position of a growing implement and, cause, whenthe growing implement is within the agricultural field, a pre-determinedagricultural treatment to be applied to the agricultural field byexecuting the agricultural prescription.